Amazon Advertising Python API Docs | dltHub

Amazon Advertising is a REST API that enables advertisers and partners to programmatically manage Amazon advertising resources and retrieve reporting data. The REST API base URL is https://advertising-api.amazon.com and All requests require OAuth2 Bearer tokens and the Amazon-Advertising-API-ClientId header..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv add "dlt[hub]" and start loading Amazon Advertising data in under 10 minutes.

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What data can I load from Amazon Advertising?

Here are some of the endpoints you can load from Amazon Advertising:

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How do I authenticate with the Amazon Advertising API?

All requests require the Authorization: Bearer <access_token> header and the Amazon-Advertising-API-ClientId header for authentication.

1. Get your credentials

1. Apply for access at the Amazon Ads API website (advertising.amazon.com/about-api) and log in with your Amazon account.
2. Complete the registration form and wait for approval.
3. Upon approval, you will receive an invitation with a sign-up link. Log in and accept the Amazon Ads API terms.
4. Navigate to the Login with Amazon (LWA) developer console and register an application to generate your Client ID and Client Secret.
5. In the Amazon Ads Advanced Tools Center, link your registered LWA application to your advertising account.
6. Generate a one-time Authorization Code by constructing an authorization URL and visiting it in your browser.
7. Exchange this Authorization Code for a long-lived Refresh Token using the /auth/o2/token endpoint. Store this Refresh Token securely along with your Client ID and Client Secret.

2. Add them to .dlt/secrets.toml

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[sources.amazon_advertising_source]
client_id = "your_lwa_client_id"
client_secret = "your_lwa_client_secret"
refresh_token = "your_refresh_token"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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uv run dlthub ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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uv run dlthub ai toolkit install rest-api-pipeline

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Amazon Advertising API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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uv run python amazon_advertising_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline amazon_advertising_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset amazon_advertising_data
The duckdb destination used duckdb:/amazon_advertising.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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uv run dlthub show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads /v2/profiles and /v2/campaigns from the Amazon Advertising API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def amazon_advertising_source(client_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://advertising-api.amazon.com",
            "auth": {"type": "bearer", "token": client_id},
        },
        "resources": [
            {"name": "attribution_reports", "endpoint": {"path": "v2/attribution/reports", "data_selector": "reports"}},
            {"name": "sponsored_products_campaigns", "endpoint": {"path": "v3/sp/campaigns", "data_selector": "campaigns"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="amazon_advertising_pipeline",
        destination="duckdb",
        dataset_name="amazon_advertising_data",
    )
    load_info = pipeline.run(amazon_advertising_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("amazon_advertising_pipeline").dataset()
sessions_df = data.attribution_reports.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM amazon_advertising_data.attribution_reports LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("amazon_advertising_pipeline").dataset()
data.attribution_reports.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Amazon Advertising data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack